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no code implementations • ICLR 2019 • Alex Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo J. Rezende

We present a soft, spatial, sequential, top-down attention model (S3TA).

no code implementations • 19 Jan 2024 • Ryan Abbott, Aleksandar Botev, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters.

no code implementations • 3 Sep 2023 • Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Phiala E. Shanahan

This Perspective outlines the advances in ML-based sampling motivated by lattice quantum field theory, in particular for the theory of quantum chromodynamics.

no code implementations • 3 May 2023 • Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions.

no code implementations • 13 Jan 2023 • Pol Moreno, Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Rosalia G. Schneider, Björn Winckler, Larisa Markeeva, Théophane Weber, Danilo J. Rezende

NeRF provides unparalleled fidelity of novel view synthesis: rendering a 3D scene from an arbitrary viewpoint.

no code implementations • 14 Nov 2022 • Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. D. G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing.

no code implementations • 18 Jul 2022 • Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban

This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant.

no code implementations • 23 Feb 2022 • Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban

In this work, we provide a numerical demonstration of robust flow-based sampling in the Schwinger model at the critical value of the fermion mass.

2 code implementations • 31 Jan 2022 • Alexander G. D. G. Matthews, Michael Arbel, Danilo J. Rezende, Arnaud Doucet

We propose Continual Repeated Annealed Flow Transport Monte Carlo (CRAFT), a method that combines a sequential Monte Carlo (SMC) sampler (itself a generalization of Annealed Importance Sampling) with variational inference using normalizing flows.

no code implementations • 4 Oct 2021 • Danilo J. Rezende, Sébastien Racanière

We are interested in the challenging problem of modelling densities on Riemannian manifolds with a known symmetry group using normalising flows.

no code implementations • 10 Jun 2021 • Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan

Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotically exact.

1 code implementation • 1 Apr 2021 • Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Soňa Mokrá, Danilo J. Rezende

We propose NeRF-VAE, a 3D scene generative model that incorporates geometric structure via NeRF and differentiable volume rendering.

no code implementations • ICCV 2021 • Daniel Zoran, Rishabh Kabra, Alexander Lerchner, Danilo J. Rezende

We present a model that is able to segment visual scenes from complex 3D environments into distinct objects, learn disentangled representations of individual objects, and form consistent and coherent predictions of future frames, in a fully unsupervised manner.

no code implementations • 21 Aug 2020 • Nan Rosemary Ke, Jane. X. Wang, Jovana Mitrovic, Martin Szummer, Danilo J. Rezende

The CRN represent causal models using continuous representations and hence could scale much better with the number of variables.

1 code implementation • 26 Jun 2020 • Adam R. Kosiorek, Hyunjik Kim, Danilo J. Rezende

An example of such a generator is the DeepSet Prediction Network (DSPN).

no code implementations • 7 Feb 2020 • Danilo J. Rezende, Ivo Danihelka, George Papamakarios, Nan Rosemary Ke, Ray Jiang, Theophane Weber, Karol Gregor, Hamza Merzic, Fabio Viola, Jane Wang, Jovana Mitrovic, Frederic Besse, Ioannis Antonoglou, Lars Buesing

In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions.

1 code implementation • NeurIPS 2019 • Alex Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo J. Rezende

Inspired by recent work in attention models for image captioning and question answering, we present a soft attention model for the reinforcement learning domain.

no code implementations • ICLR 2019 • Ananya Kumar, S. M. Ali Eslami, Danilo J. Rezende, Marta Garnelo, Fabio Viola, Edward Lockhart, Murray Shanahan

These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.

13 code implementations • 4 Jul 2018 • Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami, Yee Whye Teh

A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision.

no code implementations • 4 Jul 2018 • Dan Rosenbaum, Frederic Besse, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami

We consider learning based methods for visual localization that do not require the construction of explicit maps in the form of point clouds or voxels.

17 code implementations • ICML 2018 • Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami

Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function.

1 code implementation • ICLR 2019 • Ray Jiang, Sven Gowal, Timothy A. Mann, Danilo J. Rezende

The conventional solution to the recommendation problem greedily ranks individual document candidates by prediction scores.

1 code implementation • NeurIPS 2017 • Jörg Bornschein, andriy mnih, Daniel Zoran, Danilo J. Rezende

Aiming to augment generative models with external memory, we interpret the output of a memory module with stochastic addressing as a conditional mixture distribution, where a read operation corresponds to sampling a discrete memory address and retrieving the corresponding content from memory.

no code implementations • 15 Feb 2017 • Mevlana Gemici, Chia-Chun Hung, Adam Santoro, Greg Wayne, Shakir Mohamed, Danilo J. Rezende, David Amos, Timothy Lillicrap

We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations.

1 code implementation • 22 Feb 2016 • Andriy Mnih, Danilo J. Rezende

Recent progress in deep latent variable models has largely been driven by the development of flexible and scalable variational inference methods.

18 code implementations • NeurIPS 2014 • Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, Max Welling

The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis.

Ranked #53 on Image Classification on SVHN

no code implementations • NeurIPS 2011 • Danilo J. Rezende, Daan Wierstra, Wulfram Gerstner

We derive a plausible learning rule updating the synaptic efficacies for feedforward, feedback and lateral connections between observed and latent neurons.

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